シャドーイング練習: GitHub's AI Problem - How AI Is Damaging the Platform - 動画で英語スピーキングを学ぶ

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GitHub has an AI problem.

You may have heard about GitHub's uptime problem, but the problem is a lot more complex than you might know.

So let's talk about why AI is crushing GitHub and what the solutions might be.

Now, if you don't know me, my name is Tim Corey, and I teach people how to be an expert software developer,

not just an AI code monkey.

In today's world of just prompting for solutions, expert software development skills are more important than ever.

Otherwise, you become just another easily replaceable part with no actual skill.

That's why I provide free training here on YouTube, including multiple free courses and Patreon.

I am Tim Corey dot com.

I just wrapped up work on my game development master course, which covers how to become a game developer using

Unity and C Sharp.

So definitely check it out.

OK, let's look at the GitHub status page.

You probably heard that, you know, GitHub's been down a lot recently.

And yet, if you look at the GitHub status page, you see, well, Git operations, 99 .78%.

Now, that's not great, to be clear.

99 is only two nines of availability.

They should be shooting for four or five.

Before the Microsoft acquisition of GitHub, it was five nines.

Actually, it was zero nines.

It was 100 % uptime, but we could call it at least five nines or six nines or seven nines.

But right now, we're down to two nines in Git operations.

And you look at webhooks, 99 .66%.

That's still not great.

Two nines.

API requests, 99 .95.

That's three, actually three and a half nines.

That's pretty good.

And then issues, 99 .77 and so on.

You look at these and they're all 99 point something.

And you might say, well, that looks pretty good.

I mean, it's not four nines of availability, but it's something, right?

Well, they actually just put these percentages back up because they had taken them down.

And that really kind of hides the problem.

If you look at this and you see a lot of green

and you have some red and some yellows in here and some darker greens.

But, you know, you mostly see, you know, green.

So it looks like things are going OK.

And if you look at this, you might say, well, GitHub's not really having that much of a problem.

But then someone built the missing GitHub status page.

And this shows a whole different story.

Because when you talk about Git, you're talking about the whole, right?

If you're using GitHub, you're talking about the whole of GitHub.

And sure, if the site is down, but actions are up, that might mean that some things are still working.

But that's still a problem, right?

You still have issues.

And so, cumulatively, the GitHub platform should be a accumulation of all of those uptimes or downtimes, actually.

And based upon that, the 90 -day uptime is 87 .93%.

That is abysmal.

That is horrible.

Notice here, 93 incidents in the last 90 days.

That is, quite frankly, unacceptable.

Now, even GitHub will say that's unacceptable.

But notice that this shows a different number, right?

In fact, we come down here to get operations, which again, we go back over here, get operations.

This is this category, right?

This category says 99 .78 % on GitHub's status page.

On the missing status page, it says 98 .898%.

What's the difference?

Well, if we look here, let's come to this incident right here.

March 22nd, 2026.

There's a partial outage for 57 minutes.

We can actually look at this, let's open this up, And we'll see that this outage says disruption with some GitHub services.

Okay, on March 22nd, between 9 .05 and 10 .02, that's just under an hour, users may have experienced intermittent errors and increased latency.

Intermittent errors, if you look down here, high latency and errors.

Basically, people are timing out trying to do Git operations on GitHub.

That's a pretty significant thing because that's what you normally do with GitHub, right?

It's Git operations.

So this is a big deal, but you're saying, well, some people might have, and they use these words, try and dumb it down.

But that was March 22nd, 2026 for GitHub or Git operations, right?

So let's go to Git operations and hover over March 22nd, 2026, no downtime record on this date.

So their own status site says, yeah, we had downtime.

It wasn't for everybody, but we had downtime of some people

that had significant timeouts or significant latency connecting to and doing Git operations on GitHub.

But yet on their own status page, they don't even list it.

So that's a problem.

So when you look at this status page, you realize...

this isn't actually accurate.

This is even by their own standards is inaccurate, which is why this missing status page is a whole lot more accurate.

And even down here, you can look and see 98 .9,

98 .9, 99 .78, they actually have a higher uptime than what's listed on GitHub's status page for API requests.

But you can see 98s and some 99s in here.

So, but all told, you add all this up to this, which is the github platform you're at 87 .93

and this is why again we're talking about building software quick aside here we talk building software

and you say well you know the ai is going to write my software for me

and it's only you know it's 99 right

and that's not a problem right well 99 is right here

and i put 78 98 .9 98 .9 these different parts let's say as parts your application, they're all in the high 90s, right?

They're high 99, 98.

But when they add them all together, when you use this as a whole, the application as a whole,

your uptime is more like 87 % because these things add up, right?

They don't just live in their own little bubble where, yeah, sure, ordering's down, but that doesn't affect the other parts of your application.

Well, yeah, it kind of does because it doesn't matter if the website's up, if you can't order anything, right?

So those, the ordering affects the website.

And so there's, these are interconnected problems.

And so it actually compounds.

So when you're using a system that is mostly accurate and you do that over and over and over again, these issues and errors compound.

Okay.

Off a soapbox, off the rabbit trail here, let's get back to GitHub and let's just understand what's going on at GitHub.

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And then we'll talk about why it's happening.

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It's important to understand what's happening first.

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What's happening is they're having downtime all over.

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Now, some of us is co -pilot.

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We have code spaces, pages, packages, actions.

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They've got a lot of new stuff.

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In fact, to be clear, when Microsoft took over GitHub, GitHub was basically, now I'm simplifying it,

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but it was basically a place to put your Git repository with issues,

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a place for issues and a place for tracking or commits and those kinds of things.

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But that was basically what GitHub was.

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And then it had a little bit of actions, but not very much.

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Microsoft came in and said, Hey, let's do more with, with actions and with, with code code spaces and pages

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and co -pilot and let's broaden it and open it up to more people in the community for free.

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So there's a lot more that went into Microsoft add a lot more features and a lot more moving parts.

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And when you add a lot more moving parts, what do you get?

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Well, you get more problems, right?

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That's just the nature of the game.

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When you add more of these things to track, that reduces your downtime every time one of the components has a problem.

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So to be fair, they're doing more things, but there's more to it than that.

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And, you know, some people say, well, how do you know this is AI's problem?

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I can tell you for a fact, this is AI problem, but it's actually a couple of AI problems.

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And let's talk about each.

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First, let's talk about this right here.

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This is Kyle Daigle.

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I hope I'm saying that name right.

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He's a chief operating officer over at GitHub.

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By the way, GitHub does not have a CEO.

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Microsoft, when the last CEO left, said, hey, we're not going to add a new CEO.

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That might be adding to things that's been some speculation here that without that clear direction,

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that clear oversight, they may be floundering a bit under Microsoft's leadership.

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That may be true, that's speculation.

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But here's what

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Kyle says about the status issue, the downtimes.

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He said, yep, platform activity is surging.

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There were, listen very carefully to this, 1 billion commits in all of 2025.

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Now, 2025 was an up year because AI is starting to pick up

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and people are starting to use AI more and a lot more

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Vibe code was starting and those kind of things were happening.

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And so there is a lot more code commits being done on GitHub in 2025.

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But it's now 275 million commits per week.

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Okay.

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So 1 billion commits all of 2025 five, that's happening now in about four weeks.

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So they're on track for 14 billion commits this year.

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It was 1 billion.

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Now it's 14 billion now.

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And that's if their growth remains linear, which he said, spoiler, it won't.

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This is because of AI.

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this is because everyone is using ai

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and they're doing a lot more commits ai is a lot more verbose it does a lot more things

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and people are using things like agents and background agents

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and you know their own spun off tasks

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that run in the background all the time they're trying to see how many tokens they can use

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and they're writing a ton of code now unfortunately some that code is for existing production apps.

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I don't love that.

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And we'll see why in the future, I've got a lot more content coming around what AI is doing to our everything, to society.

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But we know that a lot of code is being written by AI in production applications,

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but there also is a lot more sample apps.

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There's a lot more, hey, is it just an app for me?

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And so the amount of code being put on GitHub has exploded.

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So 14 billion commits this year.

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Okay.

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Now, seeing the trickle -down effects, because again, if you look at all these different services, okay, so you're doing Git operations, right?

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And that's going to take a load because you're going to upload the files,

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you're going to download the files when people pull the new versions, et cetera.

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But then there's also more issues because you have more code there's more pull requests

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because you're creating more commits and you're creating more prs

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but then here's the big one actions now it depends on the repository how you set it up

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but for example in my repository whenever

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i commit a new setup changes to one of my branches it will build

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that branch and deploy it to a development server so

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if you create a new pull request on my sites than on my actual code sites.

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Then what it's going to do, it's going to build that.

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It's going to deploy it to a development server, a different one.

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It's its own standalone.

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We are using Azure.

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So I can easily see it running.

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Well, every time I create a pull request, a new one of those gets created.

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Think about if I'm creating 10 times or 100 times in a pull request, what that's doing to the number of actions that are running.

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So it's not just that there's more code or more pull requests.

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It's that there's more processing going on.

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And Microsoft integrated co -pilot into GitHub.

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And quite frankly, it's the best co -pilot Microsoft owns.

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There's so many different ones.

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This is the GitHub co -pilot.

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And it's not perfect.

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It's far from perfect, but it's the best one they have.

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And what it can do is it can do things like review pull requests automatically.

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And it can do code reviews, it can create its own pull request.

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It can do work on code.

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If you submit an issue, it can do initial work and do some initial code commits for fixing that issue.

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All of that is triggered off of issues.

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If you have more issues, you have more code, you have more work being done by Copilot.

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So the load isn't just linear with the number of pull requests is actually probably exponential

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because of all these additional things that have been added.

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So with that in mind, GitHub Actions has grown from 500 million minutes per week in 2023.

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Now that's three years ago to a billion minutes per week in 2025.

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That's last year to now 2 .1 billion minutes so far this week.

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Okay, think about that.

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So it was a billion minutes a week in 2025.

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Now it's twice as many minutes.

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Again, that's CPUs running.

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That's processes running.

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And that's 2 .1 billion.

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And that's at this time in the week.

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I don't think this was the end of the week, right?

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So there's a lot of stuff going on, right?

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Their load has dramatically increased because of AI, okay?

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OK, so we're pushing pretty hard on more CPUs, scaling services and strengthening GitHub's core features.

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OK, so that's the explanation from GitHub.

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Always take an explanation from a person in power with a big grain of salt,

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because that is their perspective of what they want you to know.

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Now, I'm not saying this person is lying.

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I'm not.

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But there's always a bit more to the story, right?

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There's a bit more that can be talked about beyond just what they say, because there's things they don't say.

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So yes, platform activity is surging.

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That's absolutely true.

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And I love the fact that he shared these numbers, because this is really important to understand that, yes.

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There is an incredible load on these servers.

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So it's not unexpected to see downtime as we stress out platforms.

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This is actually one of the big problems right now, even with AIs, where we're seeing more downtime with LLMs

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because of the fact they simply don't have enough GPUs running to support the amount of work people are asking for.

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And so they're raising prices

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and they're doing all these other things to try to figure out how to manage

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that because there isn't enough processing power for all of the demand because the demand is spiking so hard.

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So yes, that is absolutely accurate, but that doesn't mean it's the only part of the story.

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Because first of all, this downtime, again, remember, Rosie view the downtime, actual view based on their actual numbers, right?

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So this downtime didn't start since AI took over.

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It didn't start in the past 90 days.

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Now, it's gotten a lot worse in the last 90 days, but this actually started soon after

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Microsoft took over.

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And remember, Microsoft added Copilot and Codespaces and actions, well, they improved actions, a lot more of actions,

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and did some other things as well that added to the platform and added to the complexity.

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So it's natural to see some downtime from that, but it has not gotten better.

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In fact, it's getting worse.

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Now you add additional things with the additional load.

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You might say, well, that probably is enough.

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I would argue it's not enough.

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I argue it's not the whole story.

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It's a great part of the story.

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And it's, it's a great thing to say because it feels like it's right, but we're ignoring a couple of things here.

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Again, Microsoft is a heavy user of AI.

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Microsoft has been telling us for a while now

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that AI is here and we need to use AI more to build things.

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They've been saying since early 2025 that they are using AI to write more and more of their software.

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When you talk to, you know, you hear from any of the, like the head of OpenAI or the head of Anthropic,

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you'll hear them say things like, we're going to replace developers with just AI

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or AI is going to do most of the work and we should have AI write all of our code.

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These are things they're saying, but we're also seeing that the result of that is a large increase in the number of errors.

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There's a report out recently, and I'll be sharing it in a future video.

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There's a report out recently where it says that for every line of code that AI writes,

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there's three times as likely to have bugs compared to a human writing that code.

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That's stunning, right?

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That's stunning the amount of problems.

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And that's a recent report using things like Opus

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and other late gen models that are supposed to be the best at software development.

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And there's still that significant of an issue.

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So when you put that into perspective

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And you say, well, if AI was great and

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if AI really was able to solve these major problems and solve things at scale,

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then we should at least see a leveling out where,

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yeah, maybe you still have some downtime because that's going to happen under load and under stress.

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But it should be getting better because they should have written more tests and they should have fixed more problems.

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But we're not seeing that.

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We're not seeing the improvement.

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We're seeing a degradation that continues.

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And so AI is not solving this problem, is not stabilizing this environment.

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What we're seeing instead is they're increasing in the number of problems they have and significant ones.

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So there is a problem.

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Let's pull up.

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So this one right here, this happened as I'm recording this a couple of days ago.

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So, on April 23rd, between 4 p .m and 8 p .m.,

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the pull request service experienced a regression affecting merge queue operations.

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Pull requests merged via merge queue using the squash merge method produced incorrect merge commits

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when the merge group contained more than one pull request.

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In affected cases, changes from previously merged pull requests and prior commits were inadvertently reverted by subsequent merges.

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This is a five alarm fire.

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This is a major issue.

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This is not just the site was slow.

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We had to wait for an hour for the site to come back up.

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We couldn't commit because there was no, like it was down.

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This is not that.

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What this is, is the site was working but it destroyed or damaged the Git history of your commits.

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That right there is a massive problem.

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Now, to their credit, they did reach out in theory to all the people affected and said,

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hey, we're going to, here's how to fix this problem.

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We apologize to the problem, et cetera but they told them how to fix it in theory.

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But what this means is that code could have gone to production

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that was the wrong code or missing parts and trying to fix that and revert that and change that and bring it.

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That's a significant downtime for their customers.

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Now, again, of all the pull requests on this day, now this is again where numbers can really be used to manipulate.

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um again this was from uh kyle i know the tweet pulled up here

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but kyle said hey there was four million pull requests in this day

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and only 0 .07 were affected about 2700 i believe um

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which sounds like well it's not a big deal well actually even one is a big deal

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and he did say it later he's like no anything more than zero is a problem

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but if you start looking at this well it's over a few hours, four hours, not all day.

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And so your 4 million pull requests in that day was actually a smaller fraction of that.

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And it wasn't all pull requests.

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It was pull requests that were merged via the merge queue.

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So that's an even smaller number.

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And so also your percentages look a lot bigger because you're actually looking at real numbers, not just, you know, If I said,

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well, they merged 14 billion pull requests this year and only 0 .00004 % had this problem,

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that sounds tiny.

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But the reality is, it's a significant problem for a significant number of customers.

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This right here is not an issue where, oh, they had too many things going on at once.

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This was not an issue where they had, you know, too much load at the time because they had too many people doing too many things.

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This is a bug in the code.

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This is a major bug in the code.

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This is a bug in the system that should never have gotten to production.

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This is just one of the many issues you'll see in this status reports

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that can be attributed to something besides just it was load, right?

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Load is an easy thing to point to because you have all these big numbers.

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If you look at these numbers, this seriously is a huge number.

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And I can't imagine 2 .1 billion minutes of GitHub actions running in a week.

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That's just an insane amount.

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So yes, totally understand that load is a major factor and that's coming from AI usage of everybody else.

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However, that's not the only thing in the picture.

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We can see there are other issues that are going on at GitHub because of poorly written code or not checked correctly.

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And they did say, hey, we're deploying new tests to cover those areas.

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Why were there not tests beforehand?

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That's a great question because you have an AI that could have helped you write those tests in theory.

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But the reality is that when you're dealing with production applications, you can't just say, hey, let the AI do more work.

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Because what happens is you have more issues like this, your uptime looks like this, and your customers start looking elsewhere.

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And if you look online, This is what a lot of people are asking right now is, where do I go if I'm not on GitHub?

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GitHub has a stranglehold on the market.

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They have so much of the market share.

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And yet, their customers are saying, hey, what about GitLab?

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Or what about, you know, this or that?

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And all of a sudden, they can start losing real customers.

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And they might get to the point where they have real competition.

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Not because their competition was better necessarily.

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but because their competition wasn't as bad.

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And that's not a great place to be.

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So it's important that when you look at, if you look at this from your perspective, when you are writing software,

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your software, whatever part of the business it's supposed to help, the reality is your business will rely on that software.

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And if your software can't get the job done, then you're going to cause problems for your business.

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And if you sell that software, software as a service, something else, you know, then your customers will rely on that software.

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And if they can't use that software well, they're going to look somewhere else.

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And so if your job depends on, if your company's...

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Income depends on software, and it almost certainly does to one extent or another, then you need to think about, is this okay?

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Is it okay to lose customers?

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Is it okay to potentially damage our customers?

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Like this issue right here,

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they damaged their customers, and potentially those customers had problems with their own customers because of this.

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That's a big deal.

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Are you okay with that?

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Now, I encourage you to really think about how you use AI

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because of the fact that this type of thing just snowballs.

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It doesn't get better with writing more AI code.

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What happens is it gets worse and worse and worse.

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When I talked about this about a month or so ago, this was in the 90s.

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It was 92 or 3, I think it was.

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So, this has dropped another 3 % to 5 % in uptime in just the past month and a half or so.

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Like this is a major problem and it's getting worse, not better.

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Now, again, load is part of it.

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I don't want to discount that, but I do want you to think about how you write your software, writing software that is performance,

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writing software that is secure, writing software that has been tested and verified is really important.

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And just saying it's faster to let the AI write it isn't going to be good enough

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because it will be for a while.

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And then you'll see the snowball and that snowball is going to keep rolling and keep getting bigger.

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And it's going to bury you under downtime, under lost customers, under lost revenue and major issues.

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So I guess my conclusion here is don't be like GitHub, right?

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Plan for how you write good software, not just provide faster software,

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as in faster to market with new versions and new features.

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It's more important that your features work than that they're faster because getting things out there faster,

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getting more features isn't going to be helpful when they start to break down even more frequently.

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So make sure that you're writing good software.

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Make sure you know how to actually read the code that your AI writes.

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And yes, you have to verify line by line.

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And that's a problem because of the fact that AI writes so many lines of code.

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You have to actually tell it, write less lines of code.

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And that's part of being a senior developer is saying, how can we solve this problem with less lines of code?

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So you have to build up your skills so you can better drive AI.

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Otherwise, you're in danger of your entire business collapsing if you're not careful, okay?

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Thanks for watching.

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I hope that this is helpful.

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I hope that this can give you some new things to think about and new ways of approaching software development.

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But as always, I am Tim Corey.

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We'll be right back.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「GitHub's AI Problem - How AI Is Damaging the Platform」を教材にしています。繰り返し出てくる語は次のとおりです：github, code, software, request, pull。この動画には、シャドーイング用の文が379文、単語が4368語あります。音声の長さは28:19です。話す速さは1分あたり約154語で、日常会話に近い自然なペースです。単語の86%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
commit 動詞	/kəˈmɪt/	預ける, 任せる
merge 動詞	/mɝd͡ʒ/	合わせる, 合併する
solve 動詞	/sɒlv/	解決する
server 名詞	/ˈsɝvɚ/	サーバー
depend 動詞	/dɪˈpɛnd/	依存する
queue 名詞	/kju/	列, 待ち行列
deploy 動詞	/dɪˈplɔɪ/	配置する
percentage 名詞	/pə(ɹ)ˈsɛntɪd͡ʒ/	百分率, 百分比
argue 動詞	/ˈɑɹ.ɡju/	議論する, 論ずる
explanation 名詞	/ˌɛkspləˈneɪʃən/	説明
helpful 形容詞	/ˈhɛlp.fl̩/	役に立つ, 有用
rely 動詞	/ɹɪˈlaɪ/	頼る, あてにする
compound 名詞	/ˈkɑmpaʊnd/	化合物
availability 名詞	/əˌveɪ.ləˈbɪl.ɪ.ti/	供給, 可用性
frankly 副詞	/fɹæŋkli/	正直に

動画に出てくる句動詞

単語	発音	意味
take over 動詞		引き受ける
pull up 動詞	/ˌpʊl ˈʌp/	引き上げる
come down 動詞		降りる
pick up 動詞		拾う, 拾い上げる

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

First, let's talk about this right here.
I hope I'm saying that name right.
There's a lot more, hey, is it just an app for me?
Is it okay to potentially damage our customers?
Are you okay with that?

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了進行形 have/has been + -ing — 以前に始まり今も続いている動作	has been telling · They've been saying
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	are interconnected · being done · being written
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	GitHub's been · that's been · has exploded
関係詞節 who / which + 節 — 人や物について情報を加える	inaccurate, which is · this, which is

注意したい発音

話し手はyou're, they're, we'reなど、短縮形や弱形を94回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: explanation /ˌɛkspləˈneɪʃən/, potentially /pəˈtɛnʃ(ə)li/, speculation /ˌspɛk.jəˈleɪ.ʃən/, conclusion /kənˈkluːʒən/, acquisition /ˌæk.wɪˈzɪʃ.ən/
長い単語（アクセントの位置に注意）: developer /dɪˈvɛləpɚ/, explanation /ˌɛkspləˈneɪʃən/, potentially /pəˈtɛnʃ(ə)li/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, speculation /ˌspɛk.jəˈleɪ.ʃən/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: rely /ɹɪˈlaɪ/, linear /ˈlɪn.i.əɹ/, frankly /fɹæŋkli/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, carefully /ˈkɛɹf(ə)li/
/v/ — /b/ にならないように、上の歯を下唇に当てる: solve /sɒlv/, developer /dɪˈvɛləpɚ/, server /ˈsɝvɚ/, availability /əˌveɪ.ləˈbɪl.ɪ.ti/, verify /ˈvɛɹɪfaɪ/
/f/ — 「フ」ではなく、上の歯と下唇で出す: helpful /ˈhɛlp.fl̩/, frankly /fɹæŋkli/, verify /ˈvɛɹɪfaɪ/, carefully /ˈkɛɹf(ə)li/, fixing /ˈfɪksɪŋ/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。commit, merge, solveなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: GitHub's AI Problem - How AI Is Damaging the Platform - 動画で英語スピーキングを学ぶ

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繰り返し練習したい文

この動画の文法

注意したい発音

この動画での練習方法

シャドーイングとは？英語上達に効果的な理由